CS 2750 Machine Learning. Lecture 1. Machine Learning. CS 2750 Machine Learning.

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1 Lecture 1 Machine Learning Milos Hauskrecht 539 Sennott Square, x-5 Administration Instructor: Milos Hauskrecht 539 Sennott Square, x-5 TA: Zhipeng (Patrick) Luo 59 Sennott Square Office hours: TBA 1

2 Administration Study material Handouts, your notes and course readings Primary textbook: Chris. Bishop. Pattern Recognition and Machine Learning. Springer,. Administration Study material Other books: K. Murphy. Machine Learning: A probabilistic perspective, MIT Press, 1. J. Han, M. Kamber. Data Mining. Morgan Kauffman, 11. Friedman, Hastie, Tibshirani. Elements of statistical learning. Springer, nd edition, 11. Koller, Friedman. Probabilistic graphical models. MIT Press, 9. Duda, Hart, Stork. Pattern classification. nd edition. J Wiley and Sons,. T. Mitchell. Machine Learning. McGraw Hill, 1997.

3 Administration Homeworks: weekly Programming tool: Matlab (CSSD machines and labs) Matlab Tutorial: next week Exams: Midterm (March) Final (April 13-17) Final project: Written report + Oral presentation (April -) Lectures: Attendance and Activity Tentative topics Introduction to Machine Learning Density estimation. Supervised Learning. Linear models for regression and classification. Multi-layer neural networks. Support vector machines. Kernel methods. Unsupervised Learning. Learning Bayesian networks. Latent variable models. Expectation maximization. Clustering 3

4 Tentative topics (cont) Dimensionality reduction. Feature extraction. Principal component analysis (PCA) Ensemble methods. Mixture models. Bagging and boosting. Reinforcement learning Machine Learning The field of machine learning studies the design of computer programs (agents) capable of learning from past experience or adapting to changes in the environment The need for building agents capable of learning is everywhere predictions in medicine, text and web page classification, speech recognition, image/text retrieval, commercial software

5 Learning Learning process: Learner (a computer program) processes data D representing past experiences and tries to either develop an appropriate response to future data, or describe in some meaningful way the data seen Example: Learner sees a set of patient cases (patient records) with corresponding diagnoses. It can either try: to predict the presence of a disease for future patients describe the dependencies between diseases, symptoms Types of learning Supervised learning Learning mapping between input x and desired output y Teacher gives me y s for the learning purposes Unsupervised learning Learning relations between data components No specific outputs given by a teacher Reinforcement learning Learning mapping between input x and desired output y Critic does not give me y s but instead a signal (reinforcement) of how good my answer was Other types of learning: Concept learning, Active learning, Transfer learning, Deep learning 5

6 Supervised learning Data: D { d1, d,.., dn} a set of n examples d i xi, yi is input vector, and y is desired output (given by a teacher) x i Objective: learn the mapping f : X Y s.t. yi f ( xi ) for all i 1,.., n Two types of problems: Regression: X discrete or continuous Y is continuous Classification: X discrete or continuous Y is discrete Supervised learning examples Regression: Y is continuous Debt/equity Earnings Future product orders company stock price Classification: Y is discrete Label 3 Handwritten digit (array of,1s)

7 Unsupervised learning Data: D { d1, d,.., dn} di xi vector of values No target value (output) y Objective: learn relations between samples, components of samples Types of problems: Clustering Group together similar examples, e.g. patient cases Density estimation Model probabilistically the population of samples Unsupervised learning example Clustering. Group together similar examples di x i

8 Unsupervised learning example Clustering. Group together similar examples di x i Unsupervised learning example Density estimation. We want to build the probability model P(x) of a population from which we draw examples d x i i

9 Unsupervised learning. Density estimation A probability density of a point in the two dimensional space Model used here: Mixture of Gaussians Reinforcement learning We want to learn: f : X Y We see samples of x but not y Instead of y we get a feedback (reinforcement) from a critic about how good our output was input sample Learner output reinforcement Critic The goal is to select outputs that lead to the best reinforcement 9

10 Learning: first look Assume we see examples of pairs (x, y) in D and we want to learn the mapping f : X Y to predict y for some future x We get the data D - what should we do? 1 y x Learning: first look Problem: many possible functions f : X Y exists for representing the mapping between x and y Which one to choose? Many examples still unseen! y x 1

11 Learning: first look Solution: make an assumption about the model, say, f ( x) ax b y x Learning: first look Choosing a parametric model or a set of models is not enough Still too many functions f ( x) ax b One for every pair of parameters a, b y x 11

12 Fitting the data to the model We want the best set of model parameters Objective: Find parameters that: reduce the misfit between the model M and observed data D Or, (in other words) explain the data the best Objective function: Error function: Measures the misfit between D and M Examples of error functions: n Average Square Error 1 ( yi f ( xi )) n i 1 n Average misclassification error 1 1 Average # of misclassified cases n i 1 y f ( x ) i i Fitting the data to the model Linear regression problem Minimizes the squared error function for the linear model n minimizes 1 ( yi f ( xi )) n i 1 1 y x 1

13 Learning: summary Three basic steps: Select a model or a set of models (with parameters) E.g. f ( x) ax b Select the error function to be optimized n E.g. 1 ( yi f ( xi )) n i 1 Find the set of parameters optimizing the error function The model and parameters with the smallest error represent the best fit of the model to the data But there are problems one must be careful about Learning Problem We fit the model based on past examples observed in D But ultimately we are interested in learning the mapping that performs well on the whole population of examples Training data: Data used to fit the parameters of the model Training error: n 1 Error ( D, f ) ( yi f ( xi )) n i 1 True (generalization) error (over the whole population): E ( x, y )[( y f ( x)) ] Mean squared error Training error tries to approximate the true error!!!! Does a good training error imply a good generalization error? 13

14 Overfitting Assume we have a set of 1 points and we consider polynomial functions as our possible models Overfitting Fitting a linear function with the square error Error is nonzero

15 Overfitting Linear vs. cubic polynomial Higher order polynomial leads to a better fit, smaller error Overfitting Is it always good to minimize the error of the observed data?

16 Overfitting For 1 data points, the degree 9 polynomial gives a perfect fit (Lagrange interpolation). Error is zero. Is it always good to minimize the training error? Overfitting For 1 data points, degree 9 polynomial gives a perfect fit (Lagrange interpolation). Error is zero. Is it always good to minimize the training error? NO!! More important: How do we perform on the unseen data?

17 Overfitting Situation when the training error is low and the generalization error is high. Causes of the phenomenon: Model with a large number of parameters (degrees of freedom) Small data size (as compared to the complexity of the model) How to evaluate the learner s performance? Generalization error is the true error for the population of examples we would like to optimize E ( x, y )[( y f ( x)) ] But it cannot be computed exactly Sample mean only approximates the true mean Optimizing (mean) training error can lead to the overfit, i.e. training error may not reflect properly the generalization error 1 ( yi f ( xi )) n i 1,.. n So how to test the generalization error? 17

18 How to evaluate the learner s performance? Generalization error is the true error for the population of examples we would like to optimize Sample mean only approximates it Two ways to assess the generalization error is: Theoretical: Law of Large numbers statistical bounds on the difference between the true and sample mean errors Practical: Use a separate data set with m data samples to test the model (Mean) test error 1 ( y j f ( x j )) m j 1,.. m Testing of learning models Simple holdout method Divide the data to the training and test data Dataset Training set Testing set Evaluate Learn (fit) Predictive model Typically /3 training and 1/3 testing 1

19 Testing of models Data set Training set Test set case control case control Learn on the training set The model Evaluate on the test set Basic experimental setup to test the learner s performance 1. Take a dataset D and divide it into: Training data set Testing data set. Use the training set and your favorite ML algorithm to train the learner 3. Test (evaluate) the learner on the testing data set The results on the testing set can be used to compare different learners powered with different models and learning algorithms 19

20 A learning system: basic cycle 1. Data: D { d1, d,.., dn}. Model selection: Select a model or a set of models (with parameters) E.g. y ax b 3. Choose the objective function n 1 Squared error ( yi f ( xi )) n i 1. Learning: Find the set of parameters optimizing the error function The model and parameters with the smallest error 5. Testing: Apply the learned model to new data E.g. predict ys for new inputs x using learned f (x) Evaluate on the test data

CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning. Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

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